一、数据格式准备

创建一个新的文件
【在master节点中】
```
vim wordcount.txt
```

向其中放入内容并保存

hello,world,hadoop
hive,sqoop,flume,hello
kitty,tom,jerry,world
hadoop

上传到HDFS

hdfs dfs -mkdir /wordcount/   #创建文件夹
hdfs dfs -put wordcount.txt /wordcount/    #上传文件

二、创建Maven项目

pom.xml文件

<packaging>jar</packaging>
<dependencies>
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-common</artifactId>
        <version>2.9.2</version>
    </dependency>
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-client</artifactId>
        <version>2.9.2</version>
    </dependency>
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-hdfs</artifactId>
        <version>2.9.2</version>
    </dependency>
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-mapreduce-client-core</artifactId>
        <version>2.9.2</version>
    </dependency>
    <dependency>
        <groupId>junit</groupId>
        <artifactId>junit</artifactId>
        <version>RELEASE</version>
    </dependency>
</dependencies>

二、编写Mapper类

package com.ly;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import java.io.IOException;
public class WordCountMapper extends Mapper<LongWritable, Text,Text,LongWritable> { 
    //map方法就是将K1和V1转为K2和V2
    /* 参数： key :K1 行偏移量 value :V1 每一行的文本数据 context : 表示上下文对象 */
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { 
        //1:将一行的文本数据进行拆分
        String line = value.toString();
        String[] split = line.split(",");
        //2:遍历数组，组装K2和V2
        for (String word : split){ 
            //3:将K2和V2写入上下文
            context.write(new Text(word),new LongWritable(1));
        }
    }
}

Mapper继承Mapper抽象类，重写map方法

三、编写Reducer类

package com.ly;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
public class WordCountReducer extends Reducer<Text, LongWritable,Text,LongWritable> { 
    @Override
    protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException { 
        long count = 0;
        //1:遍历集合，将集合中的数字相加，得到V3
        for(LongWritable value : values){ 
            count += value.get();
        }
        //2:将K3和V3写入上下文中
        context.write(key, new LongWritable(count));
    }
}

四、编写主类

package com.ly;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
public class JobMain extends Configured implements Tool { 
    @Override
    public int run(String[] strings) throws Exception { 
        //创建一个job任务对象
        Job job = Job.getInstance(super.getConf(),JobMain.class.getSimpleName());
        //配置job任务对象
        job.setJarByClass(JobMain.class); //主类
        //第一步：指定文件的读取方式和读取路径
        job.setInputFormatClass(TextInputFormat.class);
        TextInputFormat.addInputPath(job,new Path("hdfs://192.168.239.164:9000/wordcount"));
        //第二步：设置Mapper类
        job.setMapperClass(WordCountMapper.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(LongWritable.class);
        job.setReducerClass(WordCountReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);
        job.setOutputFormatClass(TextOutputFormat.class);
        TextOutputFormat.setOutputPath(job,new Path("hdfs://192.168.239.164:9000/wordcount_out"));
        boolean b1 = job.waitForCompletion(true);
        return b1 ? 0 : 1;
    }
    public static void main(String[] args) throws Exception { 
        Configuration configuration = new Configuration();
        int run = ToolRunner.run(configuration, new JobMain(), args);
        System.exit(run);
    }
}

使用命令进行打包或IDEA的maven插件进行打包

mvn clean package

将生成的jar包上传至主机节点中的\opt目录下，放在\root路径下可能会引发权限读取问题。

五、验证结果

在上传的jar包所在路径执行以下命令：

hadoop jar wordcount-hadoop-1.0-SNAPSHOT.jar com.ly.JobMain

hadoop jar jar包名称主类的完整类名

在这里插入图片描述
可以通过web页面查看并下载结果
通过浏览器访问master节点地址：http://192.168.239.164:50070/

在选项栏选择Utilities→Browse the file system

点击wordcount_count，查看结果输出文件夹中的结果

点击part-r-00000链接在弹出的窗口中可以选择下载结果文件
在这里插入图片描述
输出结果：